Dido – .NET framework to facilitate distributed computing

Hacker News

The project site contains more documentation, but the essential concept tries to answer the question: "What if there was an API similar to the .NET Task Parallel Library but for invoking expressions on other machines, instead of just other threads?". The goal is to allow developers to write, debug, and verify a normal monolithic application, start up a few generic .NET "runner" services, and then quickly and easily add support for distributed execution of specific portions of their code on those runners, without the traditional overhead that comes with explicitly writing and orchestrating multiple unique services. I had this idea years ago but only recently found time to create a side-project to explore it. While I do not have as much free time as I'd like to work on it, it is now functional enough as a proof-of-concept to share and see if maybe there is value to someone else. At this point I'm simply curious whether the core concept has been implemented this way before, and to decide whether the idea itself along with this initial implementation are worth continued investment of time and energy to improve. Thanks to anyone providing feedback or comments!

Visit product View on Hacker NewsOct 19, 2022

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, code · Missing: agents, macos, agent

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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